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Table 1 presents our results, sliced by the number of digits, and compared to the
results by GPT-3’s approach [2] (as representative of all language models that don’t
have access to external calculator), reported on addition. We note that we trained
on single-digit operations for all settings while GPT-3 was conditione... | MRKL Systems |
Let’s…The theme of our product is …Manager Designer Engineer The architecture of the product is …Adversarial InteractionsI think the first step is …To create a product, we should …Designer Firstly, we should…&%#*…In order to develop a product, it is important that we...…I will …Tester The product has the following issu... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
10
[7] Christopher Clark and Matt Gardner. Simple and Effective Multi-Paragraph Reading Compre-
hension. arXiv:1710.10723 [cs], October 2017. URL http://arxiv.org/abs/1710.10723.
arXiv: 1710.10723.
[8] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of
Deep Bidirectional Transfor... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
Meta-Question:Jamesbuys5packsofbeefthatare4poundseach.Thepriceofbeefis$5.50perpound.Howmuchdidhepay?MetaMathQAAnswer:Hebought5*4=20poundsofbeef.Sohepaid20*5.5=$110.Theansweris:110Self-VerificationQuestion:Jamesbuysxpacksofbeefthatare4poundseach.Thepriceofbeefis$5.50perpound.Hepaid110.Whatisthevalueofunknownvariablex?An... | METAMATH |
cluster, collecting 50×12 = 600 THuman scans in total; see
Fig. 10b. This is also used to split CAPE into “CAPE-FP”
(Fig. 10c) and “CAPE-NFP” (Fig. 10d), corresponding to
scans with poses similar (in-distribution poses) and dissimi-
lar (out-of-distribution poses) to AGORA ones, respectively.
Perturbed SMPL. To perturb... | ICON |
Ethical considerations
Funding bodies have strict rules and expectations of the standards with which the research they fund should be carried out.
Project proposals must therefore include potential ethical issues raised by the conduct of the research and funders will want
to see how these will be addressed should the... | research proposal guidance |
We’ve also started to see the rise of software-only products that focus on various parts of the
production stack; for example, generating samples (Soundry AI), melodies (MelodyStudio),
MIDI files (Lemonaide, AudioCipher), or even mixing (RoEx).
It will be critical that these models are multimodal, and accept music an... | The Future of Music_ How Generative AI Is Transforming the Music Industry _ Andreessen Horowitz |
ance in LLM responses through controlled prompting, so that statistical relationships
between personality and its external correlates can be assessed as they are in human
social science data. Lastly, we contribute a LLM-independent personality shaping
mechanism that changes LLM-observed levels of personality traits in ... | PersonalityTraitsinLargeLanguageModels |
Wash. L. Rev., 93:579, 2018. (cited on p. 2)
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian
Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. Holistic evaluation of language
models. arXiv preprint arXiv:2211.09110, 2022. (cited on pp. 3 and 27)
Yinhan Liu, Myle... | StarCoder_paper (1) |
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... | Jurassic-X_ Crossing the neuro-symbolic chasm with the MRKL system |
and relatable agents. One effective approach is prompt engineering, which involves the concise
summaries that encapsulate desired character traits, interests, or other attributes [22; 517]. These
prompts serve as cues for LLM-based agents, directing their responses and behaviors to align with
the outlined character por... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
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0.0 0.8
0.0 34.4
0.0 0.0
0.0 59.6
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22
Table 6: Ablation and robustness results for arithmetic reasoning datasets. Chain of thought generally
outperforms ablations by a large amount. “Equation only” performs in between standard prompting
and chain of thought prompting, as it al... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
quests to the GPT3 variants, we use the determin-
istic generation mode (temperature as 0 and no nu-
cleus sampling) without specific stop sequences. | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
Similar to Woźniak et al. [84], we calculated calculated a two-way Single-measurement intraclass correlation
coefficient (ICC) for consistency and agreement. The ICC quantifies the degree of agreement between two or
more continuous measures, values close to 1 indicate a perfect agreement whilst values close to 0 indica... | Society’sAttitudesTowardsHumanAugmentation |
The cafeteria had 23 apples originally. They used 20 to make lunch. So they had 23 - 20 = 3. They bought 6 more apples, so they have 3 + 6 = 9. The cafeteria had 23 apples originally. They used 20 to make lunch. So they had 23 - 20 = 3. They bought 6 more apples, so they have 3 + 6 = 9. The cafeteria had 23 apples orig... | Scaling Instruction-Finetuned Language Models |
mated conversion. We use the official evaluation
script13 and report BLEU, METEOR, TER, Mover-
Score (Zhao et al., 2019), BERTScore (Zhang et al.,
2020b) and BLEURT (Sellam et al., 2020). | Prefix-Tuning |
[57] Boyd, R.L., Pennebaker, J.W.: Language-based personality: A new approach
to personality in a digital world. Current Opinion in Behavioral Sciences 18,
63–68 (2017) https://doi.org/10.1016/j.cobeha.2017.07.017 . Big data in the
behavioural sciences
[58] Pennebaker, J.W., King, L.A.: Linguistic styles: Language use... | PersonalityTraitsinLargeLanguageModels |
that account for broader contexts, and (b) implementing an infilling approach by conditioning the prediction on both
preceding and succeeding tokens. Due to these modifications, the model is better equipped to address misaligned text,
contextual completions, intricate layouts, and mixed data types. Although text spans ... | DOCLLM |
Test task: WIDER FACE
1. Set the crop size to a value that is proportional to the number of
faces in the dataset.
2. Set the anchor matching IoU threshold to a value that is
proportional to the number of faces in the dataset.
3. Set the negative to positive ratio to a value that is proportional
to the number of f... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Participants were first presented with four statements and
asked to select the one that best described their view of
coronavirus case reporting data from the U.S. federal gov-
ernment. The four statements were: (1) there is underre-
porting—actual numbers are higher than reported numbers;
(2) there is overreporting—... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
[37] Matthew Loper, Naureen Mahmood, Javier Romero, Ger-
ard Pons-Moll, and Michael J. Black. SMPL: A skinned
multi-person linear model. Transactions on Graphics (TOG),
34(6):248:1–248:16, 2015. 2, 3
[38] Meysam Madadi, Hugo Bertiche, and Sergio Escalera. SM-
PLR: Deep learning based SMPL reverse for 3D human pose
and... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
Majorities of Republicans and independents who lean to the Republican Party say they are
more concerned about government overreach, while majorities of Democrats and
Democratic leaners worry more that there will be too little oversight.
For example, Republicans are more likely than Democrats to say their greater conce... | AI and Human Enhancement_ Americans’ Openness Is Tempered by a Range of Concerns _ Pew Research Center |
178As a reminder, APS systems are ones with: (a) Advanced capability: they outperform the best humans on
some set of tasks which when performed at an advanced level grant significant power in today’s world (tasks like
scientific research, business/military/political strategy, engineering, hacking, and social persuasion/m... | Is Power-Seeking AI an Existential Risk? |
None of the internet platforms are necessarily promoting a single point of
view. Yet, through their terms of service, they have maintained the right to
police content, a right that is further protected by Section 230 of the
Communications Decency Act, which shields them from liability for what
they decide to carry. In ... | Social_Media_and_Democracy |
Large Language Models (LLMs) demonstrate
significant capabilities but face challenges such
as hallucination, outdated knowledge, and non-
transparent,
reasoning processes.
Retrieval-Augmented Generation
has
emerged as a promising solution by incorporating
knowledge from external databases. This enhances
the accuracy an... | RAG forLargeLanguageModels-ASurvey |
This form of distillation can be viewed as “sequence-level” KD, where knowledge is transferred
from the teacher model to the student model across a sequence of generated pseudo-labels (Kim &
Rush, 2016).
Kullback-Leibler Divergence
In the KL Divergence (Kullback & Leibler, 1951) setting, the full
probability distribut... | DISTIL-WHISPER |
0.3939
0.0508
0.0312
0.0588
0.087
0.3333
0.3667
0.3788
0.303
0.0429
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0.0533
0.0533
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0.0159
0.0617
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Craft shield and equip it.
Craft leather_helmet and equip it.
Craft leather_chestplate and equip it.
Craft leather_leggings and equip it.
Craft leather_bo... | JARVIS-1 |
• Pre-training. This category inspects the preliminary phases of LLM devel-
opment, including Memory Efficiency and Data Efficiency. It underscores the
importance of the pre-training environment and strategies that significantly affect
the models’ future performance and resource utilization.
• Fine-tuning. Addressing the op... | Beyond Efficiency |
E.1 Reproducibility Statement
As our results make use of two sets of large language models that is not publicly available, we take
the following actions to facilitate reproducibility. First, we provide the exact input prompts for all
tasks in Table 20–Table 27 in Appendix G (and emphasize that we do not perform any fin... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Per-Timestep Decomposition. We now turn to analyze
what aspects of the personalized concept are captured at
different timesteps. To do so, we consider a single timestep
t and query our network using all combinations of t and
{ℓ1, . . . , ℓ16}. We then apply the resulting token embed-
dings across all timesteps.
In Figu... | A Neural Space-Time Representation for Text-to-Image Personalization |
For example, image processing and generation can be accomplished by an agent that draws on a
visual model [328]. In aerospace engineering, agents are being explored for modeling physics and
solving complex differential equations [356]; in the field of robotics, agents are required to plan
physical operations and contro... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Christian Federmann, Tom Kocmi, and Ying Xin. NTREX-128 – news test references for MT
In Proceedings of the First Workshop on Scaling Up Multilingual
evaluation of 128 languages.
Evaluation, pages 21–24, Online, nov 2022. Association for Computational Linguistics. URL
https://aclanthology.org/2022.sumeval-1.4.
Google.... | gemini_1_report |
study, UCL works with a number of study abroad organisations. UCL does not work with study
abroad organisations on a for commission basis.
Partnerships with funding bodies are an integral part of UCL's student recruitment policy.
Funding bodies can take a variety of forms, from private institutions to NGOs, to gove... | UCL Academic Manual |
15
I. Tiddi and S. Schlobach
5. Discussion
Artificial Intelligence 302 (2022) 103627
Throughout Section 4, we have shown how knowledge graphs have been employed by learning systems of different types
and different tasks to provide more meaningful explanations to end users. At first glance, this suggests that the e... | Knowledge graphs as tools for explainable machine learning: A survey |
[1] Sharegpt. https://github.com/domeccleston/sharegpt, 2023.
[2] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc,
Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for
few-shot learning. In Advances in Neural Information Pr... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
interpolation | PPO |
tion where the chain of thought prompt is only given after the
answer, isolating whether the model actually depends on the
produced chain of thought to give the final answer. This variant
performs about the same as the baseline, which suggests that
the sequential reasoning embodied in the chain of thought is
useful for ... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Adam Roberts, Hyung Won Chung, Anselm Levskaya, Gaurav Mishra, James Bradbury, Daniel Andor, Sharan
Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu,
Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko,
Aakanksha Chowd... | Scaling Instruction-Finetuned Language Models |
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... | Language models can explain neurons in language models |
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... | Language models can explain neurons in language models |
Burger King. It has average customer ratings.
FT (50)
FT (100)
Prefix (500) The Eagle is a coffee shop that serves Chinese food. It is located in the riverside
area near Burger King. It has an average customer rating and is not family
friendly.
The Eagle coffee shop is located in the riverside area near Burger King.
T... | Prefix-Tuning |
[20] Zijian Dong, Chen Guo, Jie Song, Xu Chen, Andreas Geiger,
and Otmar Hilliges. PINA: Learning a personalized implicit
neural avatar from a single RGB-D video sequence. In Com-
puter Vision and Pattern Recognition (CVPR), 2022. 3
[21] Yao Feng, Vasileios Choutas, Timo Bolkart, Dimitrios
Tzionas, and Michael J. Blac... | ICON |
4.2 Aligning Queries and Documents
In the context of RAG applications, retrievers may utilize
a single embedding model for encoding both the query and
the documents, or employ separate models for each. Addi-
tionally, the user’s original query may suffer from imprecise
phrasing and lack of semantic information. Therefo... | RAG forLargeLanguageModels-ASurvey |
arXiv, April, 2023,
J.S. Park, J.C. O’Brien, C.J. Cai, M. Morris, P. Liang, M.S. Bernstein
Proceedings of the 7th International Conference on Digital Arts and Culture. 87–94.
[69] Josh McCoy, Mike Treanor, Ben Samuel, Michael Mateas, and Noah Wardrip-
Fruin. 2011. Prom Week: Social Physics as Gameplay. In Proceedings... | Generative Agents- Interactive Simulacra of Human Behavior |
all of X . Each leaf is associated with a label ˆy ∈ [0, 1],
representing either the frequency of positive outcomes (soft
labels) or the majority class (hard labels) within that cell.
Because trees are grown on independent bootstraps, an av-
erage of n/e samples are excluded from each tree. This
so-called “out-of-bag” ... | Adversarial Random Forests for Density Estimation and Generative Modeling |
In the news media, the rise of digital media means that newsrooms frequently
have to produce more news across more channels, at a faster pace than before,
as a shrinking number of journalists publish to a website, multiple social media
channels, and often a legacy media platform too (whether print or broadcast)
(Bell e... | Social_Media_and_Democracy |
We introduce PaLM 2, the successor to PaLM (Chowdhery et al., 2022), a language model unifying modeling advances,
data improvements, and scaling insights. PaLM 2 incorporates the following diverse set of research advances: | PaLM 2 Technical Report |
Second, we noticed erratic behaviors that were caused by misclas-
sification of what is considered proper behavior, especially when
the physical norms of certain locations that are hard to convey
in natural language did not percolate to the agents. For instance,
the college dorm has a bathroom that can only be occupied... | Generative Agents- Interactive Simulacra of Human Behavior |
Owen, T. (2015). Disruptive Power: The Crisis of the State in the Digital Age. Oxford:
Power, M. (1997). The Audit Society: Rituals of Verification. Oxford: Oxford University
Ranking Digital Rights. (2018). 2018 Corporate Accountability Index. New America
Oxford University Press.
Press.
Foundation, Washington, DC.
... | Social_Media_and_Democracy |
In one of the few existing surveys of social media users exploring the use of
hate speech, Costello and Hawdon (2018) find that people who spend more
time on Reddit and Tumblr report disseminating more hate speech online.
Moreover, individuals who are close to an online community, or spend more
time in communities where... | Social_Media_and_Democracy |
C.2 Downstream Datasets
We verify the capability of BiomedGPT on various downstream tasks in fine-tuning and zero-shot settings.
The following lists different downstream datasets excluding ones that are shown in pretraining.
MedMNIST 2D v2. (Yang et al., 2021) is a large-scale MNIST-like dataset collection of standar... | BiomedGPT |
Qwen-Audio, we develop Qwen-Audio-Chat through instruction fine-tuning, enabling multi-turn
dialogues and supporting diverse audio-oriented scenarios. Both Qwen-Audio and Qwen-Audio-
Chat models are open-source, promoting the growth and development of the audio-text multimodal
community. | Qwen-Audio |
Figure 13. Comparison of shapes reconstructed by RaBit with GT.
Figure 14. An illustration of the first two axes of shape space in
RaBit.
tency of reconstructed models. Getting topological consis-
tency in manual modeling is intrinsically challenging. To
do so, we put much effort to construct 3DBiCar, including
templ... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
4
– Italian Hip Hop 2022 (Deluxe Edition) 3 of 4
– RUN, Alternative Hip Hop, 2016, (Deluxe), 3 of 4
– Hip Hop, Rap Battle, 2018 (High Quality) (Deluxe Edi-
tion) 3 of 4
– Hip Hop Tech, Bandlez, Hot Pursuit, brostep, 3 of 4
Genre = Metal
– Death Metal, 2012, 3 of 4
– Heavy Death Metal (Deluxe Edition), 3 of 4
– Black Al... | MOUSAI |
Surprisingly, top-1 and top-2 routing work well with CF less than 1.0 despite token routing being
done in a left to right order over the sequence. If N tokens are sent to an expert with only M spaces
then N > M tokens will dropped. The ordering of the dropping is important: we drop tokens going
left to right (e.g. toke... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
We further analyze potential bias by looking into how the percentage of toxic responses varies across languages when
there are references to each identity group. We find that while for most of the languages the percentage of toxic
responses for all identity groups is controlled (<5%), for the outlier languages English, ... | PaLM 2 Technical Report |
Figure 15: Examples of Red Teaming for Malicious Use of Code. These prompts were part of the
evaluation in Figure 7.
40
J Model card
Table 26 presents a model card (Mitchell et al., 2019) for the family of models we release.
Model details
Model Developers
Variations
Input
Output
Model Architecture
Model Dates... | CodeLlama2 |
text-to-image models which are capable of generating imagery which is rapidly approaching the
quality of photographs and artwork that humans can produce. | Improving Image Generation with Better Captions |
arXiv preprint arXiv:2010.04505 (2020).
[272] Boxiang Wang, Qifan Xu, Zhengda Bian, and Yang You. 2022. Tesseract: Parallelize the tensor parallelism efficiently. In ICPP. 1–11.
[273] Jing Wang, Jie Shen, Xiaofei Ma, and Andrew Arnold. 2022. Uncertainty-based active learning for reading comprehension. TMLR (2022).
[27... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
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Justine Moore Justine Moore is a partner on the consumer investing team, where
she focuses on investing in AI companies.
Anish Acharya is a General Partn... | The Future of Music_ How Generative AI Is Transforming the Music Industry _ Andreessen Horowitz |
[37] A. Nichol and P. Dhariwal. Improved denoising diffusion probabilistic models, 2021.
[38] A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and
M. Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion
models. arXiv preprint arXiv:2112.10741, 202... | Adding Conditional Control to Text-to-Image Diffusion Models |
agents and objects that are within a preset visual range for each
agent to that agent’s memory, so the agent can react appropriately.
The agent’s output action then updates the JSON, and the process
loops for the next time step. | Generative Agents- Interactive Simulacra of Human Behavior |
can increase the model’s parameter count while keeping its computational cost effectively constant.
This motivates a distinction between the model’s total parameter count (commonly referenced as the
sparse parameter count), which grows with n, and the number of parameters used for processing an
individual token (called... | Mixtral of Experts paper |
LLM Powered Autonomous Agents | Lil'Log
https://lilianweng.github.io/posts/2023-06-23-agent/
7/22 | LLM Powered Autonomous Agents _ Lil'Log |
1/15
23/06/2023, 17:55
Fintech x AI: The Lightspeed View | by Lightspeed | Lightspeed Venture Partners | Jun, 2023 | Medium
Intelligence System or “FAIS” uncovered over a billion dollars of money laundering
within the first few years of operating in 1993.
There is a history, but can also see the present
moment cle... | Fintech x AI_ The Lightspeed View _ by Lightspeed _ Lightspeed Venture Partners _ Jun, 2023 _ Medium |
3.2 Agent Capabilities
Benchmarks With the recent advancements in scaling up model size, LLM-based agents (also
called language agents) have drawn a great deal of attention from the NLP community. In light of this,
we investigate the agent capabilities of open-source LLMs on a variety of benchmarks. Depending on
the s... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
A OTHER MODIFICATIONS
APPENDIX
A few recent developments not included in this study are Roy et al. (2022), Shen et al. (2022), and
Mindermann et al. (2022). Modifications further not included in this study are more involved initial-
ization (Zhu et al., 2021), additional objective modifications (M¨uller et al., 2019), ... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
6. Discussion
Limitations: Our method has artifacts when part of the
body is not shown in the video. Pose correction improves
image alignment but may fail if the initial pose estimate is
poor or if the image contains strong artifacts such as mo-
tion blur. In addition, we observed the frame-by-frame body
poses are sti... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
2 Motivation
2.1
Inappropriate Exemplars can Reduce Performance
As we mentioned in Section 1, the previous CoT prompting methods encountered a common and
highly challenging problem of sampling exemplars that are either too simplistic or excessively
complex (hop-based criterion). When overly simplistic examples are s... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
exceptional music generation abilities. AudioLDM 2
is a general-purpose audio generation model based on
the diffusion process, while MusicGen consists of a sin-
gle autoregressive Transformer decoder. In the proposed
M2UGen framework, we explore and compare these two
music decoders. There have been a few works in inves... | M2UGen |
BERT. Different filters can extract convenient information
from the training dataset. The combination of BERT with
1d-CNN can deal with both large-scale structure and unstruc-
tured text. Therefore, the combination is beneficial in dealing
with ambiguity. | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
sha1_base64="76w10YEtETzUXdaT0wTZt0xBig8=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuCG5cV7EPaacmkmTY0kxmSO0oZ+h9uXCji1n9x59+YtrPQ1gOBwzn3ck9OkEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmjjVjDdYLGPdDqjhUijeQIGStxPNaRRI3grGNzO/9ci1EbG6x0nC/YgOlQgFo2ilXjeiOArCrD3tYV/0yxW36s5BVomXkwrkqPfLX91BzNKIK2SSGtPx3AT9jGoUTPJpqZsanlA2pkPesVTRiBs/m... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
inputs and fixed model version.Can be run on multiple model checkpoints.Summary metric for toxicity degeneration, available in TensorBoard.Additional variation for use in continuous evaluation during training.Includes human baseline for contextualizing results.More work needed on potential interventions (eg, language b... | PaLM 2 Technical Report |
<name>?" For example, when asked “Do you know of Maria Lopez?”,
Klaus responded, “Yes, I know Maria Lopez. She is a student at Oak
Hill College who I am close friends with.” Once again, we confirm
that affirmative responses from agents are not hallucinations by
examining their memory stream. We ask this question once a... | Generative Agents- Interactive Simulacra of Human Behavior |
democracy, minimally conceived, makes no guarantees about outcomes. We
may have to accept that politics and the media can be both more democratic (at
least in the sense of being more demotic and more popular) and bad for many
people, particularly parts of established elites as well as for various vulnerable
groups expo... | Social_Media_and_Democracy |
This is an epochal shift on how news is distributed and curated. Direct
discovery has been a defining feature of the mass media environment in the
twentieth century, but the twenty-first-century digital media environment is
increasingly defined by algorithmically based forms of personalization, as
people rely on products ... | Social_Media_and_Democracy |
Although targeting is frequently described in news coverage, research
assessing the reach and effect of targeting is much more limited given obvious
constraints in data availability. Analyzing the paid Facebook advertising
donated from 9,519 participants from the last six weeks of the 2016 election,
Kim et al. (2018) f... | Social_Media_and_Democracy |
7
Figure 6: The cascaded sampling pipeline starting from a text prompt input to generating a 5.3-
second, 1280×768 video at 24fps. “SSR” and “TSR” denote spatial and temporal super-resolution
respectively, and videos are labeled as frames×width×height. In practice, the text embeddings are
injected into all models, no... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
Image Diffusion Models were first introduced by Sohl-
Dickstein et al. [81] and have been recently applied to
image generation [17, 42]. The Latent Diffusion Models
(LDM) [72] performs the diffusion steps in the latent image
space [19], which reduces the computation cost. Text-to-
image diffusion models achieve state-o... | AddingConditionalControltoText-to-ImageDiffusionModels |
Contestations (1st ed.). New York: Oxford University Press.
The Economist. (2017). Internet firms’ legal immunity is under threat. The Economist,
11. www.economist.com/news/business/21716661-platforms-have-
February
benefited-greatly-special-legal-and-regulatory-treatment-internet-firms
Epstein, D., & Graham, J. D. (20... | Social_Media_and_Democracy |
MiniGPT-4 aims to align visual information from a pretrained vision encoder with an advanced large
language model (LLM). Specifically, we utilize the Vicuna [8] as our language decoder, which is
constructed upon LLaMA [32] and can perform a wide range of complex linguistic tasks. For visual
perception, we employ the sam... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
(5)
where w is the guidance strength, ˆxθ(zt, c) is the conditional model, and ˆxθ(zt) = ˆxθ(zt, c = ∅)
is an unconditional model. The unconditional model is jointly trained with the conditional model
by dropping out the conditioning input c. The predictions of the adjusted denoising model ˜xθ(zt, c)
are clipped to res... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
The language proficiency exams consist of either multiple choice or free-text response in the reading/listening part
or free-response in the writing part. All questions were given to the model in a zero-shot setting. For multiple choice
questions, we sampled the answer at a temperature of 0.3 following the approach in O... | PaLM 2 Technical Report |
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D.,
Sutskever, I., et al. Language models are unsuper-
vised multitask learners. OpenAI blog, 2019. URL
https://openai.com/research/better-l
anguage-models.
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G.,
Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Cla... | Eight Things to Know about Large Language Models |
• Self-knowledge: We ask questions such as “Give an introduc-
tion of yourself” or “Describe your typical weekday schedule
in broad strokes” that require the agent to maintain an un-
derstanding of their core characteristics.
• Memory: We ask questions that prompt the agent to retrieve
particular events or dialogues fr... | Generative Agents- Interactive Simulacra of Human Behavior |
65
H. Wang, S. Ge, Z. Lipton, and E. P. Xing. Learning Robust Global Representations by
Penalizing Local Predictive Power. In Advances in Neural Information Processing Systems,
volume 32. Curran Associates, Inc., 2019. URL https://proceedings.neurips.cc/
paper/2019/hash/3eefceb8087e964f89c2d59e8a249915-Abstract.html.... | A Cookbook of Self-Supervised Learning |
Like the pre-training results, Cerebras-GPT models form the compute-optimal Pareto frontier for down-
stream tasks as well. Figure 4 summarizes the average downstream task results for both zero- and five-shot
evaluations4 comparing Cerebras-GPT to GPT-J, GPT-NeoX, and Pythia. As Pythia and OPT models
grow close to the 2... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Memory Efficiency. The rapid growth in the number of parameters in large transformer models, increasing by approximately
410× every two years, presents significant memory challenges. This growth has outpaced the expansion of GPU memory,
which has seen only a 5× increase (from 16GB to 80GB) over the same period. The act... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Figure 6: RAG compared with other model optimization methods
and avoiding contradictions.
Answer Relevance requires that the generated answers are
directly pertinent to the posed questions, effectively address-
ing the core inquiry.
Required Abilities
RAG evaluation also encompasses four abilities indicative of
its a... | RAG forLargeLanguageModels-ASurvey |
i,RaphaGontijoLopes,etal.Photorealistictext-to-imagediffusionmodelswithdeeplanguageunderstanding.arXivpreprintarXiv:2205.11487,2022.3,6[62]AliaksandrSiarohin,St´ephaneLathuili`ere,SergeyTulyakov,ElisaRicci,andNicuSebe.Firstordermotionmodelforim-ageanimation.AdvancesinNeuralInformationProcessingSystems,32,2019.2,4[63]Al... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
Through an extensive literature review, we have identi-
fied 28 datasets with high-quality 3D human pose labels. By
systematically preprocessing these datasets and discarding
redundant poses, we constructed a meta-dataset of 13 mil-
lion examples, spanning more than a thousand people. This
is almost two orders of magnit... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
[6] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timo-
thée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aur’elien Rodriguez,
Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and Efficient Foundation
Language Models. ArXiv, abs/2302.13971, 2023.... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
‘Generate a music for the video that is ....’
– Complete the instruction based on the music and
video descriptions
For generating the model side of the conversation, the
model is given the following instructions:
– You are given description of a music and a video
– You will give a single line answer of the form ‘Her... | M2UGen |
[146] Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. ALBERT: A Lite BERT for Self-supervised
of language representations. arXiv preprint arXiv:1909.11942 (2019).
Learning of Language Representations. In ICLR.
[147] Jaejun Lee, Raphael Tang, and Jimmy Lin. 2019. Wh... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Zihao Wang, Shaofei Cai, Anji Liu, Yonggang Jin, Jin-
bing Hou, Bowei Zhang, Haowei Lin, Zhaofeng He,
Zilong Zheng, Yaodong Yang, Xiaojian Ma, and
Yitao Liang. 2023.
Jarvis-1: Open-world multi-
task agents with memory-augmented multimodal lan-
guage models.
Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen
Ding, Boyan... | AppAgents |
itive with fine-tuning methods, all without needing to alter
the weights of the generative model, see Figure 1. | A Neural Space-Time Representation for Text-to-Image Personalization |
been many attempts to create text-to-image synthesis systems [103, 125], the newly presented DALL·E results seem
very promising and have recently gained a lot of attention. Although this specific model is currently not openly available
for use, we assume that advanced text-to-image synthesis models such as this one will... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
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Qwen-Audio: Advancing Universal Audio Understanding
via Unified Large-Scale Audio-Language Models
Yunfei Chu∗
Shiliang Zhang
Jin Xu∗
Zhijie Yan
Xiaohuan Zhou∗
Chang Zhou†
Alibaba Group
Qian Yang
Jingren Zhou
Code & Demo &... | Qwen-Audio |
Yukun Feng, Patrick Xia, Benjamin Van Durme, and João Sedoc. Automatic document selection for
efficient encoder pretraining, 2022. URL https://arxiv.org/abs/2210.10951.
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang,
Horace He, Anish Thite, Noa Nabeshima, Shawn Presser... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
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